Abstract
Identification of spontaneous brain activity using the electroencephalography (EEG) requires information of the frequency spectrum and the spatial distribution. The complex valued neural network (CVNN) which uses complex weights and inputs has been shown higher performance for periodic data analysis, since spectrum information is represented by complex numbers. In spontaneous EEG analysis, the phase information depends on the onset of the recording, thus it is not informative. However, the conventional CVNN is not able to remove the phase information and extract amplitude spectrum efficiently. In this paper, we introduce two activation functions for CVNN to extract the amplitude spectrum directly, and classify spontaneous EEG. Our experimental results showed that the proposed method is higher classification performance than the conventional CVNN, and comparable to the convolutional neural network (CNN). Furthermore, the proposed method showed high performance when the number of hidden units is small.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aizenberg, I., Khaliq, Z.: Analysis of EEG using multilayer neural network with multi-valued neurons. In: 2018 IEEE Second International Conference on Data Stream Mining Processing, pp. 392–396 (2018)
Goldberger, A.L., et al.: Physiobank, PhysioToolkit, and PhysioNet. Circulation 101(23), e215–e220 (2000)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: 2015 IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hirose, A.: Complex valued neural network, 2 edn. Science (2016). (In Japanese)
Kim, T., Adal, T.: Approximation by fully complex multilayer perceptrons. Neural Comput. 15(7), 1641–1666 (2003)
Nitta, T.: A back-propagation algorithm for complex numbered neural networks. In: Proceedings of 1993 International Conference on Neural Networks, vol. 2, pp. 1649–1652 (1993)
Peker, M., Sen, B., Delen, D.: A novel method for automated diagnosis of epilepsy using complex-valued classifiers. IEEE J. Biomed. Health Inf. 20(1), 108–118 (2016)
Petrantonakis, P.C., Hadjileontiadis, L.J.: A novel emotion elicitation index using frontal brain asymmetry for enhanced EEG-based emotion recognition. IEEE Trans. Inf Technol. Biomed. 15(5), 737–746 (2011)
Podmore, J.J., Breckon, T.P., Aznan, N.K.N., Connolly, J.D.: On the relative contribution of deep convolutional neural networks for SSVEP-based bio-signal decoding in BCI speller applications. IEEE Trans. Neural Syst. Rehabil. Eng. 27(4), 611–618 (2019)
Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51(6), 1034–1043 (2004)
Sunaga, Y., Natsuaki, R., Hirose, A.: Proposal of complex-valued convolutional neural networks for similar land-shape discovery in interferometric synthetic aperture radar. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11301, pp. 340–349. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04167-0_31
Trabelsi, C., et al.: Deep complex networks. In: International Conference on Learning Representations (2018)
Wolpaw, J., Wolpaw, E.W. (eds.): Brain-Computer Interfaces: Principles and Practice (English Edition), 1st edn. Oxford University Press, Oxford (2012)
Wu, R., Huang, T.: Learning of phase-amplitude-type complex-valued neural networks with application to signal coherence. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.S. (eds.) ICONIP 2017. LNCS, vol. 10634, pp. 91–99. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70087-8_10
Zhang, J., Wu, Y.: A new method for automatic sleep stage classification. IEEE Trans. Biomed. Circuits Syst. 11(5), 1097–1110 (2017)
Zhang, Z., Wang, H., Xu, F., Jin, Y.: Complex-valued convolutional neural network and its application in polarimetric SAR image classification. IEEE Trans. Geosci. Remote Sens. 55(12), 7177–7188 (2017)
Zheng, W.: Multichannel eeg-based emotion recognition via group sparse canonical correlation analysis. IEEE Trans. Cogn. Dev. Syst. 9(3), 281–290 (2017)
Acknowledgement
This work was supported by JSPS KAKENHI Grant Number 17H01760.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ikeda, A., Washizawa, Y. (2019). Spontaneous EEG Classification Using Complex Valued Neural Network. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_54
Download citation
DOI: https://doi.org/10.1007/978-3-030-36808-1_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36807-4
Online ISBN: 978-3-030-36808-1
eBook Packages: Computer ScienceComputer Science (R0)